docs: def cache_to_df(dir_path):
      """
      Convert a directory of binary array data files to a Pandas DataFrame.
  
      Parameters
      ----------
      dir_path : str
  
  
      """
      table = {}
      for attrib in glob.glob(os.path.join(dir_path, '*')):
          attrib_name, attrib_ext = os.path.splitext(os.path.basename(attrib))
          if attrib_ext == '.lf8':
              attrib_data = np.fromfile(attrib, np.float64)
              table[attrib_name] = attrib_data
  
          elif attrib_ext == '.lf4':
              attrib_data = np.fromfile(attrib, np.float32)
              table[attrib_name] = attrib_data
  
          elif attrib_ext == '.li2':
              attrib_data = np.fromfile(attrib, np.int16)
              table[attrib_name] = attrib_data
  
          elif attrib_ext == '.li4':
              attrib_data = np.fromfile(attrib, np.int32)
              table[attrib_name] = attrib_data
  
          elif attrib_ext == '.li8':
              attrib_data = np.fromfile(attrib, np.int64)
              table[attrib_name] = attrib_data
  
          elif attrib_ext == '.ib1':
              attrib_data = np.fromfile(attrib, np.bool_)
              table[attrib_name] = attrib_data
  
          elif attrib_ext.startswith('.iS'):
              length_string = int(attrib_ext[3:])
              attrib_data = np.fromfile(attrib, ('a' + str(length_string)))
              table[attrib_name] = attrib_data
  
          else:
              print('Array {} is not a recognized data type'.format(attrib))
  
      df = pd.DataFrame(table)
      return df